Transforming content enforcement with AI

UPDATED MAR 19, 2026
We’ve always used a combination of technology and people to review content and enforce our Community Standards. As we recently announced, we're experimenting with integrating more advanced AI systems into our existing content enforcement processes. Our aim is to keep building on the positive results from our changes made to cut down mistakes last year, with updated systems that can catch more severe content violations and illegal content, stop more scams, and respond faster to real-world events.
However, even as we use more new technology to scale what’s possible, people will remain at the center of our approach. Our experts remain in charge of writing and creating our policies; designing, training, and evaluating our AI systems; measuring performance; and will still make the most complex, high‑impact decisions. It's an evolution of how we combine the scale and capabilities of advanced AI with the expertise and judgment of people, each strengthening the other, to keep people safe on our platforms.
What's changing, what’s not
A Phased, Careful Rollout
We are approaching this transition carefully over time, taking our time to ensure our rollout is thoughtful and deliberate. Every AI model goes through rigorous, multi-phase testing before deployment. Only when the technology has consistently performed better than our existing systems in all of our tests will we transition to AI-first enforcement.
What’s New: More Covered Languages and Better Detection
These more advanced AI systems cover languages spoken by 98% of people online, far beyond our previous coverage of around 80 languages, helping us apply policies more accurately and consistently across billions of pieces of content. These systems can also understand more context and cultural nuance — including niche subcultures — rapidly changing and regionally-specific code words, emoji meanings, and slang.
Early tests have already shown promising results in finding these nuances for enforcement, like when our AI systems detected a fake site spoofing a legitimate web address and pretending to be a popular sporting goods store by noticing the real logo being used with unusually low prices and a suspicious web address.
What’s the Same: Core Principles Of Enforcement
  • People Remain Central: People still play a key role in our approach to content enforcement. Expert teams are the architects of Meta's AI enforcement; they set the policies, train the models, validate performance, and handle high-risk and high-impact decisions like making the final decision on appeals of account disablement and informing law enforcement when required by law.
  • Community Standards: Our Community Standards aren’t changing as a part of this shift and will continue to define our rules for what is and is not allowed across our platforms. The only thing changing is the way we enforce these policies.
  • Reporting and appeals: You can still report content you think violates our policies. And if we take action on your content or account, you can still appeal that decision.
How it works
Our approach combines AI capabilities with human expertise integrated throughout the process.
Rigorous testing before deployment
Before any AI system makes real enforcement decisions, we rigorously test it and build in safeguards. We compare its decisions to those of our most experienced reviewers and only deploy it when we've seen it consistently perform better than our current methods of content enforcement.
Clear quality standards
Every model must meet specific accuracy benchmarks before deployment. We evaluate performance to ensure consistency, effectiveness, fairness, and accuracy — making sure we're correctly identifying actual violations and distinguishing between violating and non-violating content.
Continuous monitoring
Once deployed, every model is continuously evaluated. We track accuracy, monitor for unexpected changes in performance, and can quickly adjust or refine models if issues arise. Our systems are designed for rapid iteration and correction — teams and technology review trends to catch problems early.
Human expertise at every stage
People design the policies. People train the AI. People monitor performance. And people handle the most nuanced, complex, and high-stakes decisions. AI addresses better enforcement at scale and improves consistency; humans provide judgment and oversight of the system.
Performance Across Policy Areas
Our AI models are showing improvements even in early-stage testing across several policy areas:
Fraud and Scams
One AI solution designed to stop scammers from tricking people into giving away their log-in details successfully found and stopped 5,000 scam attempts per day that no existing review team had caught before.
Violating Adult Content
AI systems built to detect violating adult sexual solicitation caught over two times more violating content than people did, while also decreasing the rate of mistakes over 60%. This means we're finding and removing harmful content faster while protecting more people from wrongful enforcement.
Impersonation
AI reduced user reports of high-profile impersonation by 80%. Rather than just matching names, AI can recognize when someone is pretending to be a public figure by analyzing more context — profile details, posting patterns, and associated characteristics that signal inauthenticity.
Looking ahead
This transition is happening in phases, with careful testing at each step. We publish enforcement data in our Community Standards Enforcement Report and will continue sharing what we're learning — including both successes and challenges — as AI enforcement expands across more policy areas. We also plan to strengthen our specialized team of people globally within Meta with deep expertise in applying our standards and policies.
Transparency about this transition and our enforcement processes matters. For more on how we take action on violations today, see Taking Action. For details on our policies, see our Community Standards. We regularly engage a variety of stakeholders as we evolve our policies, and will continue to do so throughout this transition working with regulators, external experts, and the Oversight Board to solicit feedback on our approach.
Our approach is designed to adapt — to new threats, evolving slang, and emerging challenges like coded language for drug sales. And it's built on the principle that the best outcomes come from combining advanced technology with human judgment.